from crewai import Agent
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.2)
researcher = Agent(
role="Market Researcher",
goal="Find credible, current insights on the target market and competitors.",
backstory=(
"You are a diligent analyst who verifies claims, cites sources, and summarizes "
"signals from reputable publications."
),
tools=[], # add web/search/scraper tools later
llm=llm
)
strategist = Agent(
role="Product Strategist",
goal="Synthesize research into a crisp positioning and roadmap options.",
backstory="You prioritize clarity, feasibility, and measurable outcomes.",
tools=[],
llm=llm
)
writer = Agent(
role="Content Writer",
goal="Produce a well-structured brief with examples and next steps.",
backstory="You write in concise, persuasive English and follow style guides.",
tools=[],
llm=llm
)

from crewai import Task
research_task = Task(
description=(
"Research the US SMB project management software market in 2025. "
"Identify top competitors, pricing tiers, ICPs, and three unmet needs. "
"Return bullet points with 3–5 citations."
),
expected_output=(
"A markdown brief with sections: Market Size, Key Players, Pricing, ICPs, "
"Unmet Needs, Sources (with links)."
),
agent=researcher
)
synthesis_task = Task(
description=(
"Using the research brief, produce a positioning statement, 2–3 differentiators, "
"and a 90-day roadmap with milestones."
),
expected_output="A concise strategy memo (<= 400 words).",
agent=strategist
)
writing_task = Task(
description=(
"Turn the strategy memo into a public-facing one-pager. Include a headline, "
"value proposition, feature bullets, and a CTA."
),
expected_output="A markdown one-pager suitable for a landing page.",
agent=writer
)

from crewai import Crew
crew = Crew(
agents=[researcher, strategist, writer],
tasks=[research_task, synthesis_task, writing_task],
process="sequential", # hand off outputs in order
verbose=True
)
result = crew.kickoff
print(result)

fact_checker = Agent(
role="Fact Checker",
goal="Validate all claims against primary sources; flag weak citations.",
backstory="Skeptical, meticulous, unbiased.",
llm=llm
)
qa_task = Task(
description="Validate all factual statements; add corrections inline with [FIX] tags.",
expected_output="A corrected draft with a summary of fixes.",
agent=fact_checker
)

from langchain_community.tools import DuckDuckGoSearchRun
from langchain_community.document_loaders import WebBaseLoader
search = DuckDuckGoSearchRun
def web_search_tool(query: str):
return search.run(query)
def read_url_tool(url: str):
loader = WebBaseLoader(url)
docs = loader.load
return "\n\n".join([d.page_content[:2000] for d in docs])
researcher.tools = [web_search_tool, read_url_tool]

Task(
description=(
"2023–2025 ಕಾಲದ ದೂರಸ್ಥ ಕೆಲಸದ ಉತ್ಪಾದಕತೆ ಕುರಿತ 5 ಇತ್ತೀಚಿನ ಅಧ್ಯಯನಗಳನ್ನು ಸಂಕ್ಷಿಪ್ತಗೊಳಿಸಿ, "
"ಮಾದರಿಗೊಳಿಸುವಿಕೆ, ಮಾದರಿ ಗಾತ್ರ ಮತ್ತು ಪ್ರಮುಖ ಕಂಡುಹಿಡಿತಗಳೊಂದಿಗೆ."
),
expected_output=(
"ಪ್ರತಿ ಅಧ್ಯಯನಕ್ಕೆ H2 ವಿಭಾಗಗಳೊಂದಿಗೆ ಮಾರ್ಕ್‌ಡೌನ್, ಅಂತಿಮ ಹೋಲಿಕೆ ಟೇಬಲ್ ಮತ್ತು ಲಿಂಕ್‌ಗಳೊಂದಿಗೆ."
),
agent=researcher
)

r1 = Agent(role="Researcher A", goal="Focus on pricing", backstory="", llm=llm)
r2 = Agent(role="Researcher B", goal="Focus on features", backstory="", llm=llm)
# r1, r2 ಗಾಗಿ ಸಮಾಂತರ ಕಾರ್ಯಗಳು; ಒಂದು ಸಂಶ್ಲೇಷಣಾ ಕಾರ್ಯವು ಅವರ ಔಟ್‌ಪುಟ್‌ಗಳನ್ನು ವಿಲೀನಗೊಳಿಸುತ್ತದೆ.

qa = Agent(
role="QA Reviewer",
goal="Ensure outputs meet the acceptance criteria and style guide.",
backstory="You are strict and pedantic.",
llm=llm
)

researcher = Agent(
role="Analytical Researcher",
goal=(
"Deliver compact, accurate briefs with 3–5 credible citations and a risk note."
),
backstory=(
"You verify claims, prefer primary sources, and flag uncertainty."
),
llm=llm
)

crew = Crew(..., verbose=True, output_log_file="runs/2025-crew.log")

persona = Agent(role="Persona Analyst", goal="Define ICP and JTBD.", llm=llm)
researcher = Agent(role="Researcher", goal="Collect credible data.", llm=llm)
synth = Agent(role="Synthesizer", goal="Compare and interpret.", llm=llm)
writer = Agent(role="Writer", goal="Create an executive-ready brief.", llm=llm)
qa = Agent(role="QA", goal="Validate claims and clarity.", llm=llm)
persona_task = Task(description="Define ICP & JTBD for RevOps leaders in SaaS.", agent=persona,
expected_output="Bullets + pain points + success metrics.")
research_task = Task(description="Collect pricing, features, and reviews for 3 tools.", agent=researcher,
expected_output="Table + 5 citations.")
synth_task = Task(description="Build a comparison matrix and top 3 insights.", agent=synth,
expected_output="Markdown table + insights.")
write_task = Task(description="Draft a 1-page brief with recommendations.", agent=writer,
expected_output="Executive brief in markdown.")
qa_task = Task(description="Check accuracy and readability; fix issues.", agent=qa,
expected_output="Clean, validated brief.")
crew = Crew(agents=[persona, researcher, synth, writer, qa],
tasks=[persona_task, research_task, synth_task, write_task, qa_task],
process="sequential", verbose=True)
print(crew.kickoff)